Compression of 3-D point clouds using hierarchical patch fitting

Robert A. Cohen, Maja Krivokuca, Chen Feng, Yuichi Taguchi, Hideaki Ochimizu, Dong Tian, Anthony Vetro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For applications such as virtual reality and mobile mapping, point clouds are an effective means for representing 3-D environments. The need for compressing such data is rapidly increasing, given the widespread use and precision of these systems. This paper presents a method for compressing organized point clouds. 3-D point cloud data is mapped to a 2-D organizational grid, where each element on the grid is associated with a point in 3-D space and its corresponding attributes. The data on the 2-D grid is hierarchically partitioned, and a Bezier patch is fit to the 3-D coordinates associated with each partition. Residual values are quantized and signaled along with data necessary to reconstruct the patch hierarchy in the decoder. We show how this method can be used to process point clouds captured by a mobile-mapping system, in which laser-scanned point locations are organized and compressed. The performance of the patch-fitting codec exceeds or is comparable to that of an octree-based codec.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages4033-4037
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - Jul 2 2017
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sep 17 2017Sep 20 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period9/17/179/20/17

Keywords

  • Mobile mapping systems
  • Patch fitting
  • Point cloud compression

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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